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Down-Sized Initialization for Optimization-Based Unstructured Trajectory Planning by Only Optimizing Critical Variables
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AI
DOI：10.1109/TIV.2022.3163335.png)
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Abstract
En 中文
Optimization-based trajectory planners for an autonomous vehicle have received increasing attention due to their convenience for handling various complex constraints. However, their formulated trajectory planning optimization problems in unstructured environments are highly non-convex and nonlinear, requiring a proper initial guess to help with convergence. Given the low efficiency of common sampling-and-search-based initialization strategies due to the dimensionality brought by complex vehicle dynamics and the maneuverable space narrowed by cluttered obstacles, the sequential optimization of a series of softened intermediate problems is emerging as a new initialization strategy. Unlike sequential optimization-based strategies, which optimize all decision variables (w.r.t. the positions, velocities, vehicle orientations, steering angles, and so on of all waypoints) for initialization, the proposed strategy only optimizes critical variables, whose values should help satisfy the inequalities strictly with a small equation violation. Thus, the original scheme for initialization is decoupled into two sub-tasks with down-sized variables. One reduces the nonlinear equation violation, and the other ensures strict inequality satisfaction. Experimental results show that the proposed initialization strategy can facilitate the solution of an optimization-based trajectory planner, thereby making the trajectory planner outperform the state-of-the-art competitors in this community.
Keywords:
Trajectory
Optimization
Trajectory planning
Kinematics
Planning
Space vehicles
Manipulators
Autonomous driving
intelligent vehicle
trajectory planning
numerical optimal control
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